Expanding Canonical Spider Silk Properties through a DNA Combinatorial Approach

Jaleel, Zaroug; Zhou, Shun; Martín‐Moldes, Zaira; Baugh, Lauren; Yeh, Jonathan R.; Dinjaski, Nina; Brown, Laura T.; Garb, Jessica E.; Kaplan, David L.

doi:10.3390/ma13163596

Cited by 10 publications

(6 citation statements)

References 66 publications

(100 reference statements)

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“…Higher elastic moduli were seen in the samples from library A compared to those of library B. In comparison with natural silk proteins, both libraries had higher elastic moduli (Jaleel et al, 2020). In other research, a chimeric protein of Flag-AcSp1 was expressed in E. coli with a molecular mass of 36.8 kDa.…”

Section: Bacterial Systemsmentioning

confidence: 93%

Recombinant Spider Silk: Promises and Bottlenecks

Ramezaniaghdam

Nahdi

Reski

2022

Front. Bioeng. Biotechnol.

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Spider silk threads have exceptional mechanical properties such as toughness, elasticity and low density, which reach maximum values compared to other fibre materials. They are superior even compared to Kevlar and steel. These extraordinary properties stem from long length and specific protein structures. Spider silk proteins can consist of more than 20,000 amino acids. Polypeptide stretches account for more than 90% of the whole protein, and these domains can be repeated more than a hundred times. Each repeat unit has a specific function resulting in the final properties of the silk. These properties make them attractive for innovative material development for medical or technical products as well as cosmetics. However, with livestock breeding of spiders it is not possible to reach high volumes of silk due to the cannibalistic behaviour of these animals. In order to obtain spider silk proteins (spidroins) on a large scale, recombinant production is attempted in various expression systems such as plants, bacteria, yeasts, insects, silkworms, mammalian cells and animals. For viable large-scale production, cost-effective and efficient production systems are needed. This review describes the different types of spider silk, their proteins and structures and discusses the production of these difficult-to-express proteins in different host organisms with an emphasis on plant systems.

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Section: Bacterial Systemsmentioning

confidence: 93%

Recombinant Spider Silk: Promises and Bottlenecks

Ramezaniaghdam

Nahdi

Reski

2022

Front. Bioeng. Biotechnol.

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“…The properties of native spider silk vary within and across species due to the presence of different genes encoding a variety of silk proteins with conserved repetitive core domains [ 96 ]. The latter can be used as building blocks for new silk-based biomaterials with varying mechanical properties.…”

Section: Recombinant Spider Silkmentioning

confidence: 99%

Recombinant Proteins-Based Strategies in Bone Tissue Engineering

et al. 2021

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The increase in fracture rates and/or problems associated with missing bones due to accidents or various pathologies generates socio-health problems with a very high impact. Tissue engineering aims to offer some kind of strategy to promote the repair of damaged tissue or its restoration as close as possible to the original tissue. Among the alternatives proposed by this specialty, the development of scaffolds obtained from recombinant proteins is of special importance. Furthermore, science and technology have advanced to obtain recombinant chimera’s proteins. This review aims to offer a synthetic description of the latest and most outstanding advances made with these types of scaffolds, particularly emphasizing the main recombinant proteins that can be used to construct scaffolds in their own right, i.e., not only to impregnate them, but also to make scaffolds from their complex structure, with the purpose of being considered in bone regenerative medicine in the near future.

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“…40,41 By combining sequences from different silk types with varying ratios of β-sheet, β-turn, and random coil content, the mechanical properties of the resulting materials were expanded. 42 In this paper, we report a deep learning model to predict proteins' overall secondary structure content directly from the sequence of amino acids, focusing on the overall content in a given sequence (see Figure 1). On the basis of training data collected from the Protein Data Bank 43 (125 955 structures, details see Materials and Methods section), our model learns the hidden features as patterns of secondary structures.…”

Section: Introductionmentioning

confidence: 99%

“…Another example of how control of secondary structure can be applied to biomaterial design includes fine-tuning of the material′s mechanical properties. Both the mechanical strength and extensibility of silk proteins are determined by the total amount of both β-sheet nanocrystals and noncrystalline amorphous regions within the protein, respectively. , By combining sequences from different silk types with varying ratios of β-sheet, β-turn, and random coil content, the mechanical properties of the resulting materials were expanded …”

Section: Introductionmentioning

confidence: 99%

End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins

Chen

Chiang

et al. 2022

ACS Biomater. Sci. Eng.

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Structural proteins are the basis of many biomaterials and key construction and functional components of all life. Further, it is well-known that the diversity of proteins’ function relies on their local structures derived from their primary amino acid sequences. Here, we report a deep learning model to predict the secondary structure content of proteins directly from primary sequences, with high computational efficiency. Understanding the secondary structure content of proteins is crucial to designing proteins with targeted material functions, especially mechanical properties. Using convolutional and recurrent architectures and natural language models, our deep learning model predicts the content of two essential types of secondary structures, the α-helix and the β-sheet. The training data are collected from the Protein Data Bank and contain many existing protein geometries. We find that our model can learn the hidden features as patterns of input sequences that can then be directly related to secondary structure content. The α-helix and β-sheet content predictions show excellent agreement with training data and newly deposited protein structures that were recently identified and that were not included in the original training set. We further demonstrate the features of the model by a search for de novo protein sequences that optimize max/min α-helix/β-sheet content and compare the predictions with folded models of these sequences based on AlphaFold2. Excellent agreement is found, underscoring that our model has predictive potential for rapidly designing proteins with specific secondary structures and could be widely applied to biomedical industries, including protein biomaterial designs and regenerative medicine applications.

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Expanding Canonical Spider Silk Properties through a DNA Combinatorial Approach

Cited by 10 publications

References 66 publications

Recombinant Spider Silk: Promises and Bottlenecks

Recombinant Spider Silk: Promises and Bottlenecks

Recombinant Proteins-Based Strategies in Bone Tissue Engineering

End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins

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